1. Field of the Invention
The present invention generally relates to the use of infrared imaging and, in particular, to the use of three dimensional infrared anatomical imaging to identify individuals.
2. Background Description
Standardized Medical Imaging
Imaging Sensor Technologies
Several medical imaging technologies are in common use:
X-ray images such as from mammographs and chest X rays are produced from X rays passing through and being attenuated by the patient's body tissues to produce a 2D projection image. The density at a point in the image represents the tissue densities through which the x-rays passed. X-ray imagery shows bone structure and fatty tissue.
Computed Tomography (CT) or Computed Axial (Computer Assisted) Tomography (CAT) have the same source as conventional X-rays. Multiple X-rays are taken at different angles to the patient and mathematically reconstructed 3D images are produced. Contrast agents supplied to the patient can aid in imaging soft tissue.
Magnetic Resonance Imaging (MRI) is produced through absorption of energy from Radio Frequency (RF) pulses when excited nuclei return to their original state. Images of tissue hydrogen concentration are produced that reflect the different structures imaged. MRI is considered noninvasive, provides high-resolution images, and is much safer than imaging using X rays. However it is expensive and generally requires a longer scanning time than CT. Functional MRI (fMRI) images provide both structural and performance information.
Magnetic Resonance Angiography (MRA) is a specific type of MRI that produces an image of blood flow for the visualization of arteries and veins.
Digital Subtraction Angiography (DSA) produces images of a patient's blood vessels as the difference image between a post- and a pre-contrast injection images. Since the contrast medium injected flows only in the vessels, the image data arising from other structures does not change in the two images and are eliminated by the subtraction.
Ultrasound imaging uses pulsed or continuous high-frequency sound waves to image internal structures by recording the different reflecting signals. Among others, ultrasound imaging is used in echocardiography for studying heart function and in prenatal assessment. Although ultrasonographic images are typically not high-resolution as images obtained through CT or MRI, they are widely adopted because of ultrasound's non-invasiveness, cost effectiveness, acquisition speed, and harmlessness.
Nuclear medicine acquisition methods such as Single Photon Emission Computed Tomography (SPECT), and Positron Emission Tomography (PET) are functional imaging techniques. They use radioactive isotopes to localize the physiological and pathological processes rather than anatomic information.
Digital X-ray systems have recently been developed which take whole body images in less than 15 seconds vs. 45 minutes or more for conventional X-rays that imaged only a portion of the body at a time. The new systems, developed for routine scanning of workers in South African diamond mines, expose persons to 75 percent less radiation than a conventional fullbody X-ray series.
All produce images oriented to body-centric models, but fusion of images from multiple sensor modalities is neither automatic nor precise. Some techniques involve injection of contrast agents or use of potentially harmful radiation. 3D/IR does not. In addition, 3D/IR is much less expensive to purchase, maintain, and operate; is portable for rapid deployment to thermal triage at accident locations; and its images automatically contain features for patient identification.
Medical Image Segmentation
Segmenting an anatomical structure in a medical image amounts to identifying the region or boundary in the image corresponding to the desired structure. Segmentation is beneficial when applied to image data of both patients with pathology and normals used for comparison to define abnormality. Segmentation in IR images shares technical issues with MRI images. In particular, locating blood vessels, determining centerline location and branch point locations, treating hidden segments and apparent discontinuities.
Manually segmenting a structure in three-dimensional data is prone to errors. Experts cannot visualize the entire 3D data collection simultaneously and so resort to outlining the structure of interest manually in a series of consecutive two-dimensional slices out of the original 3D volume. This slice-by-slice approach to manual segmentation is time consuming and generally suffers from poor reproducibility of results for a given analyst, as well as variations among analysts.
Visualization Techniques in Medical Imaging
Radiographic imaging of coronary arterial structure plays a crucial role both in diagnosing and treating patients who are at risk of heart disease. In order to exploit the information generated by current clinical methods in coronary arteriography, it is necessary for the physician to build a mental model of both the three dimensional (3D) arterial structure and of the non-rigid motion that this structure undergoes as it moves with the beating heart. This mental model must be constructed from sequences of two dimensional (2D) images obtained from the x-ray projection process. The image data acquired is noisy and often difficult to interpret.
To facilitate clinical decision-making process, computational algorithms have been developed for the purpose of generating a structural representation that is better suited for understanding and visualization by the physician. The primary focus of these algorithms has been to detect salient image features and then complete a segmentation of the angiographic 2D image. The segmented images can then be used to build and label a 3D model of the vascular system that can be interactively studied by the physician as it undergoes the motion associated with the beating heart's cycle.
Methods for automated blood vessel enhancement and segmentation have been developed for angiographic image sequences to assist surgeons and physicians in visualizing the vascular system. While the methods were developed for x-ray, ultrasound, MRI, and other active sensors, they can be applied to analyzing thermal infrared image frames and sequences.
O'Brien and Ezquerra apply temporal, spatial, and structural constraints to automate segmentation of coronary vessels in angiographic image sequences. Their methods perform automated segmentation from sequences of biplanar x-ray angiograms by imposing an integrated set of constraints based on the anatomical structure of the vascular system, temporal changes in position due to motion, and spatial coherence.
Frangi examines the multiscale second order local structure of an image (Hessian) to develop a vessel enhancement filter. His vesselness measure is obtained on the basis of all eigenvalues of the Hessian. Its clinical utility is shown by the simultaneous noise and background suppression and vessel enhancement in maximum intensity projections and volumetric displays. Accurate visualization and quantification of the human vasculature is an important prerequisite for a number of clinical procedures. Grading of stenoses is important in the diagnosis of the severity of vascular disease since it determines the treatment therapy. Interventional procedures such as the placement of a prosthesis in order to prevent aneurysm rupture or a bypass operation require an accurate insight into the three-dimensional vessel architecture.
Both two-dimensional projection techniques, such as DSA, and three-dimensional modalities such as X-ray rotational angiography, CTA and MRA are employed in clinical practice. Although CTA and MRA provide volumetric data, the common way of interpreting these images is by using a maximum intensity projection. The main drawbacks of maximum intensity projections are the overlap of non-vascular structures and the fact that small vessels with low contrast are barely visible. This has been a main limitation in time-of-flight MRA. In contrast enhanced MRA, the delineation of these vessels is considerably improved, but other organs can be still projected over the arteries.
A vessel enhancement procedure as a preprocessing step for maximum intensity projection display will improve small vessel delineation and reduce organ over-projection. Segmentation of the vascular tree will facilitate volumetric display and will enable quantitative measurements of vascular morphology. There are several approaches to vessel enhancement. Some of them work at a fixed scale and use (nonlinear) combinations of finite difference operators applied in a set of orientations. Orkisz presents a method that applies a median filter in the direction of the vessel. All these methods have shown problems to detect vessels over a large size range since they perform a fixed scale analysis. Multi-scale approaches to vessel enhancement include cores, steerable filters, and assessment of local orientation via eigenvalue analysis of the Hessian matrix.
The multiscale approach was inspired by Sato and Lorenz who use the eigenvalues of the Hessian to determine locally the likelihood that a vessel is present. They modify the approach by considering all eigen-values and giving the vesselness measure an intuitive, geometric interpretation. They regard vessel enhancement as a filtering process that searches for geometrical structures that can be regarded as tubular.
When intra-arterial contrast material is injected, a reference image is first acquired without contrast, and then subtracted from the image with contrast for background suppression. If no motion artifacts are present, the subtracted images are of such good quality, that further processing is not desirable. They therefore only apply their enhancement filter to the contrast images directly, and use the subtracted images to judge the performance of the vessel enhancement filter. In the peripheral vasculature, performance of subtraction is usually quite good. Although contrast is not very high in the contrast images, the method detects most vessels, over a large size range. Since vessels appear in different sizes it is important to introduce a measurement scale that varies within a certain range. By determining the vesselness of the MRA image at multiple scales, separate images are obtained depicting vessels of various widths. Small and large vessels can be distinguished, which is used in artery/vein segmentation.
Image Standardization and Encoding
Computer processing can be used to ‘normalize’ MRI scans using programmed functions to reorient the angle, position, size, etc of the scan to standard stereotaxic space. Normalized images are much easier to read, because the slices match those available in published atlases. In addition, the majority of modern functional imaging studies normalize their scans to standardized space. Therefore normalized patient scans are easier to interpret relative to each other, to atlases, and to functional imaging studies.
Normalization routines seek to minimize differences between sections of the patient's image and corresponding sections from templates generated from healthy patients. Patient anomalies such as a lesion can distort the results. Non-linear normalization routines may try to minimize the anomaly by compressing that region—distorting the area of the image in the process and causing imprecision in location and size of the lesion in the normalized image. Using only linear normalization results in less risk of significant local distortion, providing better overall coregistration, although not achieving optimal local registrations.
The linear functions of translation, rotation, zoom and shear can be applied in each of the three dimensions. For example, rotation can be applied in the x, y and z coordinates, correcting yaw, pitch and roll of the input image). Any three points that are colinear in the input image will also be colinear in the output image, although two parallel lines in an input image may not be parallel after an affine transformation. Also, all of the 12 linear parameters (translation, zoom, rotation and affine functions each in the x, y and z dimensions) are computed based on information from the entire image—therefore these functions are rarely disrupted by anomalies such as lesions found in MRI scans. Unlike with linear functions, points that are colinear in the input image will not necessarily be colinear after nonlinear normalization. Furthermore, nonlinear functions are more heavily influenced by local image information, and therefore nonlinear normalization of patient scans may lead to distortion due to the unusual appearance of the anomaly. For this reason, it is necessary to mask lesions and clip artifacts when estimating a nonlinear normalization from MRI.
MRI systems commonly provide that two or more images can be yoked together to compare coregistration with each other or with a template, or to compare the effects of different normalization techniques. Displays automatically present corresponding slices of yoked images even if the images are sliced to different thicknesses or are cropped to show overlapping but different areas. The user must make the necessary adjustments manually.
Most MRI systems export scans in DICOM or a proprietary format. Most fMRI studies will generate a vast number of two dimensional images that are stacked to create three dimensional volumes. Not all scanners use the same axial slices, and different scanners use different methods for saving images.
DICOM and Health Level (HL7) standards and integration standard (IHE) Integrating the Healthcare Enterprise address aspects of the technology development needed for searchable databases and automated comparison of imagery. DICOM's hanging protocols (HP) arrange sets of images by group guided by preferences of the site, section, or user. The protocol can be specific for anatomic region, laterality, procedure code, and reason for the procedure. They can be environment specific in terms of number of displays. Processing treatment can be specified also.
For a facility to develop a consistent HP display, it must establish a protocol for how that examination is performed. There can be significant variability among radiologists in a subspecialized section where different radiologists perform different MR sequences to image the same anatomy. Many institutions use the same name for examinations of multiple anatomic regions—for example an MR of the ankle is called MR lower extremity, as is an MR of the knee or the tibia/tibula. Different system vendors often have different naming conventions for the same series.
Biometric Identification
Role of Biometrics
Increased reliance on digital patient records, including imagery, requires techniques for insuring that records are correctly associated with the correct patient. At the same time, patient privacy must be insured against identity theft and access control procedures must restrict viewing and modifying patient records to only authorized persons. The integrity of data and images within a searchable library must be maintained.
Biometric identification answers the question “Who are You?” by extracting physiological parameters unique to each person. Current biometric systems are primarily deployed to recognize persons who want to be recognized and who actively cooperate in their enrollment and subsequent recognition. Controlled tests of automated realtime systems have used subject populations under 100,000, required active cooperation, and achieved performance figures that justify using the systems to augment human guards. However, even when used in a verification mode (one-to-one matching) resulting error rates are too high to justify unattended access control into secure areas. When used to screen for persons on a Watch List (one-to-many matching) the resulting false alarm rates create unacceptable delays if the Watch List contains more than a few dozen people. Integrating multiple biometrics (in particular combining face recognition and fingerprint matching) and upgrading visual face recognition to 3D can improve system accuracy, but may degrade system speed and reliability to unacceptable levels.
Limitation of Current Biometric Technologies
Current biometric technologies are not secure against sophisticated adversaries intent on forging another person's biometric identity. Visual Facial Recognition Systems used with a human attendant can be defeated by makeup and disguise. Automated, unattended systems may be defeated through “before the lens tampering”, by presenting to the system camera a photograph or hand-held video display of an authorized person—avoiding the need to wear makeup or disguise.
Livescan Fingerprint Systems can be defeated with or without the cooperation of an authorized person. Unattended systems may be defeated by reactivation of a residual print. Because fingers leave residual oils on a fingerprint sensor, blowing hot breath on the sensor, placing a water-filled balloon on top, or illuminating with a halogen flashlight may reactivate a previously-left authorized print. Another approach is to obtain a latent fingerprint from an authorized person and place it on the sensor. A synthetic peel-off finger covering can be molded from a stolen print. Yokohama University reported a method for producing silicon or GummiBear synthetic fingers from latent prints or directly from the authorized person's finger. Use of synthetic finger coverings would likely not be detected unless a human guard visually inspected each person's fingers at the time the sensor was used.
Photographs of an authorized person's iris can be collected without his knowledge by using high-resolution digital video imagery to capture from a distance an image of the eye. Only a single frame is required. The iris pattern is then printed on a mask or imprinted on a contact lens to impersonate the authorized person.
A trusted biometric system must be able to distinguish real and forged biometric features. Ideally, it would be able to identify a person in spite of his attempts at forging another identity. At a minimum, it must detect all forgery attempts. Various techniques for improving the ability of biometric systems to detect attempts at forgery will continue to be made as the value associated with successful biometric identity theft continues to increase. Any biometric system relying on features that can be covertly collected and forged cannot guarantee security against future forgery methods. In order to trust biometric identification systems for applications involving national security, the features used must be impervious to forgery even through sophisticated means such as radical surgery. For positive identification within very large populations, the underlying physiology must insure that the features are unique for each person.
Biometric identification based on the use of subsurface anatomical features provides security against biometric identity theft because the features are not readily observable to would-be forgers. If the sensor is unknown or difficult to obtain, that provides additional security for the system. Even if the sensor or the anatomical information from an authorized person is easy to obtain, it should be impossible for one person to surgically alter his anatomical features to mimic those of another person without leaving evidence that he has done so. Facial surgery that was sufficiently extensive to fool a 3D/IR system would necessarily pose a significant risk of death.
To meet requirements for cost, speed, convenience, and automation, biometric systems often use a reduced feature set and use procedures and sensors that might not distinguish between actual features and forged features. This is the case with fingerprint sensors. Acceptance of the uniqueness of fingerprints was based upon rolled inked prints taken by a trained technician who would repeat the collection procedure if the quality of the prints obtained was inadequate. The transition to automated livescan sensors in theory offers the same or better potential image quality; however, the self-imaging procedure does not provide consistent quality images and the users may not be trained or motivated to assure maximum quality.
Accuracy, speed, reliability, and system cost are not the only parameters important in rating biometric systems; population coverage, scalability, security, user convenience, and life cycle cost must also be addressed. Current biometric systems give unacceptable performance when large populations are enrolled; even when lighting conditions are ideal and even when there is no attempt at sophisticated disguises or forged identities. When biometric technology is reduced to practice, care must be taken to maintain as much as possible of the inherent uniqueness, persistence, scalability, and quality of the characterizing features for that biometric. This is best accomplished when the sensors do not require contact with the subject and are self-regulating with respect to focus, contrast, and other imaging parameters.
Face Recognition
Visual face recognition techniques require controlled lighting, are vulnerable to normal daily changes in appearance as well as to intentional disguise, and do not have sufficient feature complexity or density for scalability to a billion people. Other biometrics such as iris scanning or fingerprints require close or contact distances and cannot be performed in realtime with non-cooperating subjects. 3D/IR-ID offers faster throughput, greater security against identity theft, greater potential for covert use under darkness, and ability to match against legacy databases including mug shots, driver licenses, and passport photos.
Depending on the number of persons to be identified, use of highly precise IR and range imagers, together with precise standardization, can reduce the numbers of features required for positive unique ID. Using some number of large features over a large area of the face can yield the same performance as using a greater number of smaller features over a smaller area. Requirements for distance, system cost, level of cooperation, expected pose variations, population size, accuracy requirements, speed of processing, file sizes, Face Code size, and exportability of the technology influence the size threshold of features considered.
Pentland utilized eigenanalysis of visual faces to develop a set of characteristic features. Faces are then described in terms of weightings on those features. The approach claims to accommodate head position changes and the wearing of glasses, as well as changes in facial expressions. Pentland remarks that pre-processing to produce a canonical form of the face including only the area from just above the eyebrows to just below the mouth and with width equal to that height is essential to eigenvector recognition systems. Processing to automate that task and establish the eigenvector set is extensive, especially for large databases.
There is increasing need for automated face recognition systems that are scalable to very large populations and accurate under a broad range of ambient conditions. Two-dimensional visual face recognition cannot provide the required performance when lighting varies, an extended period of time intervenes, or there is a change in appearance caused by aging, illness, cosmetics, disguise, or surgery. Three-dimensional visual face recognition, using structured light or coherent laser radar, provides a partial solution to reducing errors associated with variations in lighting and head pose. ID can be based on the visible or range data rotated into a standard pose.
Visible metrics require ground truth distance measurements unless they rely strictly upon ratios of measurements. In that case, they are dependent upon precise designation of landmark positions within visible feature areas. For example, designation of eye corners, nose tip, nostrils, and ear-head locations entail surface areas that encompass many pixels in the visual image. Variations in landmark selection points create significant differences in scaling and can be seriously affected by intentional disguises, facial expressions, makeup, sunburns, shadows and similar unintentional disguises. Detecting the wearing of disguises and distinguishing between identical twins can generally not be done from visible imagery (2D or 3D) or range imagery from a distance.
Biometric identity theft may be prevented by technology that performs identification using internal anatomical features that cannot be forged by one person attempting to be identified as someone else without significant risk of death. Prior patents of Prokoski relied upon the complexity of the subsurface anatomical features extractable from infrared imagery to assure uniqueness for each person's pattern of features. Identification based on infrared imagery was offered as a robust biometric approach that is not vulnerable to biometric identity theft.
Infrared Face and Body Parts Biometrics
The identification of persons from infrared images is known in the art as evidenced by Prokoski U.S. Pat. No. 5,163,094 to the present inventor, which discloses a method and apparatus for analyzing closed thermal contours, called “elemental shapes” which are created by the vascular system interacting with the anatomical structure. Fifty or more elemental shapes can be identified for example in a human face imaged with an IR camera that has an NETD (noise equivalent thermal difference) of 0.07° C. and a spatial resolution of 256×256 pixels. Characteristics of those shapes, such as the centroid location and ratio of area to perimeter, remain relatively constant regardless of the absolute temperature of the face, which varies with ambient and physiological conditions. Two infrared images are compared by comparing the characteristics of corresponding shapes. A distance metric is defined and calculated for each pair of images. If the value is within a threshold, the two images are considered to be from the same person. Claims cover use of thermal contours for identification of faces and body parts. Preferred embodiment includes use of template matching from areas of the face that have little variation due to facial expression changes.
Thermal Infrared identification based on thermal contour analysis of subjects who are not wearing eyeglasses, or who always wear the same eyeglasses, has been shown to be useful for identification against medium size databases, defined as 100,000 to 1,000,000 images, when the subject actively cooperates or is induced to cooperate such as by the need to look at a red/green light in order to proceed through a metal detector arch. However, thermal contour analysis of IR facial images is not sufficiently accurate or fast when used against very large databases or when imagery to be compared has different positions of the subject's head, or different temperature distributions, since those produce significant changes to the thermal contours used for identification. In order to reduce errors associated with changes to the edges of thermal contours, using smaller characterizing features increases the likelihood that a sufficient number of features will remain unaffected by changes in head position, eyeglasses, hairstyle, and temperature.
Prokoski's 1992 publication of research utilized a principal components analysis of thermal shapes found in facial thermograms. The resulting accuracy of 97% equals or surpasses the results reported by Pentland with visible facial images. Prokoski's training database, furthermore, included identical twins and involved non-cooperative imaging of about 200 persons. Thus, the head sizes and orientations were not pre-determined as they were in the Pentland study. As a result, the use of eigenanalysis of thermal shapes is concluded to be more robust than the use of eigenanalysis of visual facial features. However, the basic requirements of eigenanalysis still pertain of course to their use in matching of thermal images by consideration of inherent elemental shapes. That is, the approach is computationally intensive, requires a pre-formed database, and requires standardization of the images through pre-processing.
Prokoski U.S. Pat. No. 6,173,068 discloses a method and apparatus for extracting and comparing thermal minutiae corresponding to specific vascular and other subsurface anatomical locations from two images. Minutiae may be derived from thermal contours, or may be absolutely associated with specific anatomical locations that can be seen in the thermal image, such as the branching of blood vessels. Each minutia is then associated with a relative position in the image and with characteristics such as apparent temperature, the type of branching or other anatomical feature, vector directions of the branching, and its relation to other minutiae.
The comparison of thermal minutiae from two facial images is analogous to the comparison of sets of fingerprint minutiae, in that two images are said to identify the same person if a significant subset of the two sets are found to correspond sufficiently in relative positions and characteristics. Classification of the facial thermograms can be performed to partition a database and reduce the search for matching facial patterns. Alternately, encoding of the minutiae patterns offers a unique FaceCode that may be repeatably derived from each person, minimizing the need for searching an image database.
Effective biometrics must be fast, accurate, foolproof, easy to use, provide full population coverage, be free of racial or ethnic bias, and work even on non-cooperating subjects. Current biometric techniques including: hand geometry, fingerprints, retina and iris scanning, voice recognition, and visual facial recognition each has inherent technical limitations that prevent satisfying all those requirements.
Both eyewitness and automated face recognition suffer from vulnerability to lighting changes. In very dim light, or darkness, neither can identify faces. Many automated face recognition system use an IR illuminator to bounce invisible light off the skin. The resulting image provides some feature measurements, but little detail. This is IR-illuminated visual face recognition, not Infrared Face Recognition. The term “IR image” refers to the output from focal plane array or scanning sensors sensitive to emissions within the 3-14 micron range. The IR camera is totally passive, emitting no energy or other radiation of its own, but merely collecting and focusing the thermal radiation spontaneously and continuously emitted from the surface of the human body.
Infrared cameras with low enough MRTD (minimum resolvable temperature difference) are able to directly sense the difference between skin immediately overlaying the superficial blood vessels, and adjacent skin. An MRTD of 0.04° C. is sufficient for locating large vessels. Different people in the same environment may have average face temperatures differing by six degrees or more; and an individual's face may display a 10 degree variation in temperature, producing saturated areas in the imagery, with resulting loss of data, if the camera has insufficient bandwidth. Variations in depth and width along a blood vessel, coupled with large local changes in surface temperature, and the blurring effect of thermal conduction create a blotchy surface thermal pattern containing few if any curvilinear structures, and no apparent minutiae. Novel processing steps are required to extract consistent curvilinear features and to extract characterizing minutiae.
In addition to the novel processing steps, the camera optics, array size, instantaneous field of view of the detectors and fill factor must combine to produce clear definition of vascular features on the order of 2 mm wide, with consistent skeletonization and minutiae extraction. Biometric identification based on comparison of thermal vascular segments or thermal minutiae is akin to fingerprint ridge or minutiae matching, and compares favorably to the computationally intensive approaches involving Eigenfaces or Hidden Markov Method comparisons by other biometric approaches using visual or infrared images.
Development of the body's highly complex vascular structure has been modeled from embryo angiogenesis and tissue repair angiogenesis. Using any of the various models, the number of branches and nodes, and randomness of the growth patterns of branches, yields estimates for the uniqueness of vascular patterns for each person over any sizable area of his body. Comparison with the quantitative uniqueness of fingerprints as determined by Pankati indicates that vascular patterns of portions of the body are more unique than fingerprints.
All current biometrics have serious inherent limitations in non-cooperative identification against very large databases. In particular, iris and retinal scanning, hand and fingerprints require active cooperation by the subject at close distance and are therefore relatively slow, require training, and cannot be used for covert or non-cooperative identification. Face recognition based on visual images can be performed from long distances without cooperation, but is prone to changes in illumination, intentional disguises, and similarity in appearance of many people. Two-dimensional face recognition based on infrared imaging as invented by Prokoski improved upon the limitations of visual face recognition, but use against very large populations required scaling based upon 2D projection estimates of three-dimensional landmarks.
Prokoski's previous patents for infrared identification used analysis of thermal contours, thermal minutiae, autowaves, IR-visual correlation, and direct comparison of infrared images standardized as to scale, orientation and histogram. Given suitable IR camera optics, array size, instantaneous field of view of the detectors, fill factor, and bandwidth, the patented processing methods provide clear definition of vascular features on the order of 1 mm wide. Comparison of thermal vascular segments or thermal minutiae is computationally akin to fingerprint ridge or minutiae matching, and compares favorably to computationally intensive approaches involving Eigenfaces or Hidden Markov Method comparisons of facial images.
Prokoski U.S. Pat. No. 6,173,068 teaches the use of thermal minutiae for identification. Encoding uses location relative to standard axes and landmarks, and may also include type, connectedness, vectors, and other parameters. The resulting template that identifies an individual is not compact. A compressed binarized 2D image of the vasculature offers greater compaction with less processing and is more forgiving of variances in the sensor data. Claims cover use of structural data obtained from infrared and other medical imaging sensors for identification of patients and other subjects. Preferred embodiment defines as minutiae the branch points and apparent end points of blood vessels in the face as seen in passive infrared images, and uses these minutiae to identify the person. Fingerprint minutiae-matching techniques can be used for the matching step.
The number of minutiae and area of distribution is much greater for facial thermal minutiae than for a fingerprint. Also, there is a larger variance in width of facial vessels compared to little variance in fingerprint ridge spacing. Furthermore, fingerprint ridge lines are constrained by the boundaries of the fingers to be concentric, whereas no such restriction applies to interior facial vessels. The degrees of freedom relating to facial thermal minutiae are therefore significantly greater than the degrees of freedom for fingerprint minutiae. Following the Supreme Court Decision in Daubert vs Merrell Dow Pharmaceuticals (92-102), 509 U.S. 579 (1993), a number of court cases have raised the issue that no scientific basis has been established for the claim that fingerprints are unique other than statistical analysis of observations. Therefore, there has been no scientific basis for claiming that two partial fingerprints are from the same person if they contain a specific number of similar minutiae characteristics. Concerted efforts are underway at various universities to derive those scientific bases for fingerprints. We expect that some number of those efforts will succeed and will lay a foundation for analyzing the uniqueness and scalability of infrared facial minutiae.
Prokoski U.S. Pat. No. 6,920,236 teaches overlay and combination of visual and infrared imagery for identification to reduce the limitations of each sensor.
Prokoski U.S. Pat. No. 6,850,147 teaches encoding of a biometric template for seamless access and transmission of identification.
Prokoski U.S. Pat. No. 6,529,617 teaches the use of thermal minutiae to stabilize positioning of a patient.
Prior art includes developments made by this inventor to use curvilinear features from infrared images to identify persons by template matching of the feature maps; by minutiae matching of minutiae derived from intersections, branchings, and apparent endings of the treelike feature maps; and by FaceCodes which are standardized representations of the feature maps and/or minutiae. Those developments utilized two-dimensional imagery for identification based on template matching and extracted codes. The IR FaceCode production of the present inventor's prior art included classification of all curvilinear features extracted from the thermal image into vascular, skin folds, facial hair, and other.